The purpose of this research was to illustrate the use of sensor-derived spatial data in generating management classifications and applying variable-rate vineyard mechanization to improve vine balance and fruit quality. In a commercial 'Concord' vineyard in Westfield, New York, data from proximal sensors were used to generate spatial maps of soil apparent electrical conductivity, canopy Normalized Difference Vegetation Index (NDVI), and crop weight. Local block kriging was used on all spatial data layers after removal of outliers to predict values at common grid points which approximated the vineyard row and vine spacing so that relationships between data layers could be interrogated to determine which layers were indicative of overall vineyard production. Cluster analysis (k-means) was used to generate three management classifications and stratified manual viticulture sampling was used to predict the crop size and vine size in each region. Crop load is the relationship between vine fruit yield (crop size) and vine vegetative growth (vine size). The Ravaz Index (RI) is a practical indicator of crop load through the measurement of the crop weight - pruning weight ratio. In this study, RI was predicted mid-season in each management classification and crop load was adjusted through mechanized variable-rate fruit thinning. To achieve variable-rate fruit thinning, a spatial prescription map was generated and interfaced with precision agriculture hardware/software which controlled the hydraulic flow to the shaker head on a mechanical harvester. On-the-go variable-rate fruit thinning shifted the population mean 34% toward target RI values and decreased the standard deviation by 30% indicating that the vineyard was more balanced and more uniform at harvest.